@inproceedings{kolyvakis-etal-2018-deepalignment,
title = "{D}eep{A}lignment: Unsupervised Ontology Matching with Refined Word Vectors",
author = "Kolyvakis, Prodromos and
Kalousis, Alexandros and
Kiritsis, Dimitris",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1072/",
doi = "10.18653/v1/N18-1072",
pages = "787--798",
abstract = "Ontologies compartmentalize types and relations in a target domain and provide the semantic backbone needed for a plethora of practical applications. Very often different ontologies are developed independently for the same domain. Such {\textquotedblleft}parallel{\textquotedblright} ontologies raise the need for a process that will establish alignments between their entities in order to unify and extend the existing knowledge. In this work, we present a novel entity alignment method which we dub DeepAlignment. DeepAlignment refines pre-trained word vectors aiming at deriving ontological entity descriptions which are tailored to the ontology matching task. The absence of explicit information relevant to the ontology matching task during the refinement process makes DeepAlignment completely unsupervised. We empirically evaluate our method using standard ontology matching benchmarks. We present significant performance improvements over the current state-of-the-art, demonstrating the advantages that representation learning techniques bring to ontology matching."
}
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<abstract>Ontologies compartmentalize types and relations in a target domain and provide the semantic backbone needed for a plethora of practical applications. Very often different ontologies are developed independently for the same domain. Such “parallel” ontologies raise the need for a process that will establish alignments between their entities in order to unify and extend the existing knowledge. In this work, we present a novel entity alignment method which we dub DeepAlignment. DeepAlignment refines pre-trained word vectors aiming at deriving ontological entity descriptions which are tailored to the ontology matching task. The absence of explicit information relevant to the ontology matching task during the refinement process makes DeepAlignment completely unsupervised. We empirically evaluate our method using standard ontology matching benchmarks. We present significant performance improvements over the current state-of-the-art, demonstrating the advantages that representation learning techniques bring to ontology matching.</abstract>
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%0 Conference Proceedings
%T DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors
%A Kolyvakis, Prodromos
%A Kalousis, Alexandros
%A Kiritsis, Dimitris
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F kolyvakis-etal-2018-deepalignment
%X Ontologies compartmentalize types and relations in a target domain and provide the semantic backbone needed for a plethora of practical applications. Very often different ontologies are developed independently for the same domain. Such “parallel” ontologies raise the need for a process that will establish alignments between their entities in order to unify and extend the existing knowledge. In this work, we present a novel entity alignment method which we dub DeepAlignment. DeepAlignment refines pre-trained word vectors aiming at deriving ontological entity descriptions which are tailored to the ontology matching task. The absence of explicit information relevant to the ontology matching task during the refinement process makes DeepAlignment completely unsupervised. We empirically evaluate our method using standard ontology matching benchmarks. We present significant performance improvements over the current state-of-the-art, demonstrating the advantages that representation learning techniques bring to ontology matching.
%R 10.18653/v1/N18-1072
%U https://aclanthology.org/N18-1072/
%U https://doi.org/10.18653/v1/N18-1072
%P 787-798
Markdown (Informal)
[DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors](https://aclanthology.org/N18-1072/) (Kolyvakis et al., NAACL 2018)
ACL
- Prodromos Kolyvakis, Alexandros Kalousis, and Dimitris Kiritsis. 2018. DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 787–798, New Orleans, Louisiana. Association for Computational Linguistics.